AWS Machine Learning Blog

SEPTEMBER 27, 2024

Specifically, we cover the computer vision and artificial intelligence (AI) techniques used to combine datasets into a list of prioritized tasks for field teams to investigate and mitigate. Data preparation SageMaker Ground Truth employs a human workforce made up of Northpower volunteers to annotate a set of 10,000 images.

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Data Science Dojo

JULY 17, 2023

These methods can help businesses to make sense of their data and to identify trends and patterns that would otherwise be invisible. In recent years, there has been a growing interest in the use of artificial intelligence (AI) for data analysis. It is similar to TensorFlow, but it is designed to be more Pythonic.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

This session covers the technical process, from data preparation to model customization techniques, training strategies, deployment considerations, and post-customization evaluation. Explore how this powerful tool streamlines the entire ML lifecycle, from data preparation to model deployment.

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ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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AWS Machine Learning Blog

DECEMBER 24, 2024

Fine tuning Now that your SageMaker HyperPod cluster is deployed, you can start preparing to execute your fine tuning job. Data preparation The foundation of successful language model fine tuning lies in properly structured and prepared training data. The following is the Python code for the get_model.py

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Towards AI

JANUARY 31, 2023

Identifying Traditional Nigerian Textiles using Artificial Intelligence on Android Devices ( Part 1 ) Nigeria is a country blessed by God with 3 major ethnic groups( Yoruba, Hausa, and Ibo) and these different ethnic groups have their different cultural differences in terms of dressing, marriage, food, etc.

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IBM Data Science in Practice

JANUARY 9, 2024

By Carolyn Saplicki , IBM Data Scientist Industries are constantly seeking innovative solutions to maximize efficiency, minimize downtime, and reduce costs. One groundbreaking technology that has emerged as a game-changer is asset performance management (APM) artificial intelligence (AI).

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AWS Machine Learning Blog

MARCH 8, 2023

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. 1 with the following additions: The Snowflake Connector for Python to download the data from the Snowflake table to the training instance.

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AWS Machine Learning Blog

APRIL 26, 2024

FM-powered artificial intelligence (AI) assistants have limitations, such as providing outdated information or struggling with context outside their training data. This feature empowers you to rapidly synthesize this information without the hassle of data preparation or any management overhead.

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Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps. Norvig, Artificial Intelligence: A Modern Approach, 4th ed.

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Flipboard

NOVEMBER 21, 2023

MATLAB   is a popular programming tool for a wide range of applications, such as data processing, parallel computing, automation, simulation, machine learning, and artificial intelligence. Verify your python3 installation by running python -V or python --version command on your terminal.

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AWS Machine Learning Blog

AUGUST 1, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. See the following notebook for the complete code sample.

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ODSC - Open Data Science

SEPTEMBER 25, 2023

Tapping into these schemas and pulling out machine learning-ready features can be nontrivial as one needs to know where the data entity of interest lives (e.g., customers), what its relations are, and how they’re connected, and then write SQL, python, or other to join and aggregate to a granularity of interest.

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AWS Machine Learning Blog

JULY 11, 2024

It supports all stages of ML development—from data preparation to deployment, and allows you to launch a preconfigured JupyterLab IDE for efficient coding within seconds. Specifically, we demonstrate how you can customize SageMaker Distribution for geospatial workflows by extending it with open-source geospatial Python libraries.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

SageMaker Studio allows data scientists, ML engineers, and data engineers to prepare data, build, train, and deploy ML models on one web interface. Finally, we deploy the ONNX model along with a custom inference code written in Python to Azure Functions using the Azure CLI. image and Python 3.0

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Towards AI

JUNE 27, 2023

In the rapidly expanding field of artificial intelligence (AI), machine learning tools play an instrumental role. Its seamless integration capabilities make it highly compatible with numerous other Python libraries, which is why Scikit Learn is favored by many in the field for tackling sophisticated machine learning problems.

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Towards AI

APRIL 17, 2025

In this piece, we explore practical ways to define data standards, ethically scrape and clean your datasets, and cut out the noise whether youre pretraining from scratch or fine-tuning a base model. Nericarcasci is working on LEO, a Python-based tool that acts like a conductor for AI. 👉 Read the post here!

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AWS Machine Learning Blog

JULY 29, 2024

With the addition of forecasting, you can now access end-to-end ML capabilities for a broad set of model types—including regression, multi-class classification, computer vision (CV), natural language processing (NLP), and generative artificial intelligence (AI)—within the unified user-friendly platform of SageMaker Canvas.

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AWS Machine Learning Blog

AUGUST 26, 2024

GenASL is a generative artificial intelligence (AI) -powered solution that translates speech or text into expressive ASL avatar animations, bridging the gap between spoken and written language and sign language. This instance will be used for various tasks such as video processing and data preparation.

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AWS Machine Learning Blog

JULY 11, 2024

Fine tuning embedding models using SageMaker SageMaker is a fully managed machine learning service that simplifies the entire machine learning workflow, from data preparation and model training to deployment and monitoring. Python script that serves as the entry point. client('s3') # Get the region name session = boto3.Session()

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Airflow for workflow orchestration Airflow schedules and manages complex workflows, defining tasks and dependencies in Python code.

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AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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AWS Machine Learning Blog

DECEMBER 13, 2023

We create an automated model build pipeline that includes steps for data preparation, model training, model evaluation, and registration of the trained model in the SageMaker Model Registry. About the Authors Dr. Romina Sharifpour is a Senior Machine Learning and Artificial Intelligence Solutions Architect at Amazon Web Services (AWS).

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AWS Machine Learning Blog

JUNE 17, 2024

Being one of the largest AWS customers, Twilio engages with data and artificial intelligence and machine learning (AI/ML) services to run their daily workloads. The training data used for this pipeline is made available through PrestoDB and read into Pandas through the PrestoDB Python client.

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Towards AI

MAY 8, 2024

Created by the author with DALL E-3 Google Earth Engine for machine learning has just gotten a new face lift, with all the advancement that has been going on in the world of Artificial intelligence, Google Earth Engine was not going to be left behind as it is an important tool for spatial analysis.

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Towards AI

APRIL 21, 2025

It details the necessary setup, data preparation requirements, the step-by-step fine-tuning workflow, methods for leveraging the resulting custom models, and illustrative examples of potential use cases. For Python development, the official mistralai library needs to be installed.

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Pickl AI

NOVEMBER 5, 2024

Introduction Data Science and Artificial Intelligence (AI) are at the forefront of technological innovation, fundamentally transforming industries and everyday life. Enhanced data visualisation aids in better communication of insights. Domain knowledge is crucial for effective data application in industries.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

You can use this notebook job step to easily run notebooks as jobs with just a few lines of code using the Amazon SageMaker Python SDK. Data scientists currently use SageMaker Studio to interactively develop their Jupyter notebooks and then use SageMaker notebook jobs to run these notebooks as scheduled jobs.

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AWS Machine Learning Blog

JULY 18, 2023

If you are prompted to choose a Kernel, choose the Python 3 (Data Science 3.0) Import the required Python library and set the roles and the S3 buckets. You now run the data preparation step in the notebook. In your Studio notebook, open the spam_detector.ipynb notebook. kernel and choose Select.

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. From deriving insights to powering generative artificial intelligence (AI) -driven applications, the ability to efficiently process and analyze large datasets is a vital capability.

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Dataconomy

AUGUST 16, 2023

” The answer: they craft predictive models that illuminate the future ( Image credit ) Data collection and cleaning : Data scientists kick off their journey by embarking on a digital excavation, unearthing raw data from the digital landscape. Interprets data to uncover actionable insights guiding business decisions.

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Becoming Human

MAY 15, 2023

One is a scripting language such as Python, and the other is a Query language like SQL (Structured Query Language) for SQL Databases. Python is a High-level, Procedural, and object-oriented language; it is also a vast language itself, and covering the whole of Python is one the worst mistakes we can make in the data science journey.

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AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring.

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AWS Machine Learning Blog

MAY 25, 2023

The container image contains codes to invoke the SageMaker Serverless Inference endpoints, and necessary Python libraries to run the Lambda function such as NumPy, pandas, and scikit-learn. For more information, refer to Granting Data Catalog permissions using the named resource method. We have completed the data preparation step.

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AWS Machine Learning Blog

AUGUST 3, 2023

Solution overview To efficiently train and serve thousands of ML models, we can use the following SageMaker features: SageMaker Processing – SageMaker Processing is a fully managed data preparation service that enables you to perform data processing and model evaluation tasks on your input data.

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